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{Component} from 'preact'\nimport Split from \"../components/old/split\";\nimport Latex from \"../components/Latex\";\nimport Panel from \"../components/old/panel\";\nimport Example from \"../components/example\";\nimport Todo from \"../components/old/todo\";\nimport Minus from \"../components/old/minus\";\nimport Plus from \"../components/old/plus\";\nimport Code from \"../components/old/code\";\nimport Timer from \"../components/old/timer\";\nimport Image from \"../components/Image\";\n\nconst r = String.raw;\n\nexport default class OttimizzazioneLineare extends Component {\n render() {\n return (\n
\n Ho rimosso il rumore in sottofondo da tutti i video di Ricerca Operativa!\n
\n \n\n La funzione obiettivo è la funzione con valore noto sconosciuto:\n
\n\n
\n Funzione della funzione obiettivo che indica la direzione del suo aumento più veloce.\n
\n\n
\n
\n Convertiamo ogni equazione
\n Convertiamo le disequazioni in equazioni aggiungendo una variabile slack.\n
\n\n Sostituiamo le variabili potenzialmente negative (unconstrained)
\n La funzione da minimizzare/massimizzare, tipicamente indicata con una
\n Un modo per rappresentare sistemi in forma standard, anche noto come matrice equivalente completa del sistema.\n
\nTN | \n||||
---|---|---|---|---|
\n Variabili che hanno tutti 0 e un 1 nella loro colonna del tableu.\n
\n\n La loro controparte sono le variabili fuori base.\n
\n\n Un sistema lineare è risolto quando tutte le variabili originali (
\n Un algoritmo per massimizzare efficientemente variabili di sistemi lineari, derivato da Gauss-Jordan.\n
\n\n Se il problema è rappresentato in forma di tableau, allora esso è risolvibile applicando l'algoritmo di Gauss-Jordan, in aggiunta tenendo conto delle regole per la selezione delle variabili entranti e uscenti.\n
\nEx_LP_testo
con il Simplex:\n \n
\n Un estensione del Simplex per permettere la risoluzione di problemi con termini noti negativi.\n
\n\n Prevede l'introduzione di un problema ausiliario.\n
\n